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Use signal processing metrics to create machine learning functionality for EEG-Notebooks and compare clustering algorithms. We can use scikit t-SNE optimization techniques (https://scikit-learn.org/stable/modules/manifold.html#optimizing-t-sne), Pyriemann Embedding (https://pyriemann.readthedocs.io/en/latest/auto_examples/ERP/plot_embedding_EEG.html#sphx-glr-auto-examples-erp-plot-embedding-eeg-py), diffusion_map (https://pydiffmap.readthedocs.io/en/master/reference/diffusion_map.html), and other ideas (anything else?)
Follow my work on Twitch: https://www.twitch.tv/hussainather
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Finishing up the MLapproaches.ipynb file to turn it into a page of the documentation for the NeurotechX repository.
MLapproaches.ipynb
.py
Awesome!
I suggest call the final file
examples/visual_n170/04r__n170_clustering.py
Thanks! Would you like me to add pyDiffMap to the requirements.txt file?
pyDiffMap
requirements.txt
@HussainAther I believe this is now summarized in the PR
#58
We'll review that and resume comments there :)
JohnGriffiths
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Use signal processing metrics to create machine learning functionality for EEG-Notebooks and compare clustering algorithms. We can use scikit t-SNE optimization techniques (https://scikit-learn.org/stable/modules/manifold.html#optimizing-t-sne), Pyriemann Embedding (https://pyriemann.readthedocs.io/en/latest/auto_examples/ERP/plot_embedding_EEG.html#sphx-glr-auto-examples-erp-plot-embedding-eeg-py), diffusion_map (https://pydiffmap.readthedocs.io/en/master/reference/diffusion_map.html), and other ideas (anything else?)
Follow my work on Twitch: https://www.twitch.tv/hussainather
The text was updated successfully, but these errors were encountered: